Document Type
Article
Publication Date
4-21-2023
Department
Department of Computer Science
Abstract
Rough set theory places great importance on approximation accuracy, which is used to gauge how well a rough set model describes a target concept. However, traditional approximation accuracy has limitations since it varies with changes in the target concept and cannot evaluate the overall descriptive ability of a rough set model. To overcome this, two types of average approximation accuracy that objectively assess a rough set model’s ability to approximate all information granules is proposed. The first is the relative average approximation accuracy, which is based on all sets in the universe and has several basic properties. The second is the absolute average approximation accuracy, which is based on undefinable sets and has yielded significant conclusions. We also explore the relationship between these two types of average approximation accuracy. Finally, the average approximation accuracy has practical applications in addressing missing attribute values in incomplete information tables.
Publication Title
CAAI Transactions on Intelligence Technology
Recommended Citation
Kong, Q.,
Wang, W.,
Zhang, D.,
&
Zhang, W.
(2023).
Two kinds of average approximation accuracy.
CAAI Transactions on Intelligence Technology.
http://doi.org/10.1049/cit2.12222
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/17085
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© 2023The Authors. CAAI Transactions on Intelligence Technology published by John Wiley& Sons Ltd on behalf of The Institution of Engineering and Technology and Chongqing University of Technology. Publisher’s version of record: https://doi.org/10.1049/cit2.12222